Why the study?
Many heart rate detection algorithms require prior information about test subjects, creating a need for an algorithm capable of detecting the heart rate of previously unknown species without a priori knowledge.
A novel adaptive RR interval detection algorithm accurately identifies species and detects heart rate without prior knowledge, achieving 99.71% accuracy in human ECGs.
Hypothesis-generating for species-agnostic HR detection; requires prospective validation before clinical use.
Many algorithms require prior information about test subjects for heart rate (HR) detection. In this paper, we propose a novel algorithm for detecting the HR of previously unknown species based on the Pan-Tompkins (PT) algorithm without a priori knowledge. In this improved PT algorithm, some parameters that need to be predefined can be adaptively adjusted according to the recognition of previous species. In the recognition step, a clustering algorithm is applied to obtain the rough RR intervals, and a decision tree is applied for species recognition by using two features: the rough RR intervals and the proportion of the ECG power of frequency that is less than 5 Hz. The accuracy and the Kappa of species classification in the recognition step can reach 93.15% and 90.23%, respectively. In the HR detection step, the improved PT algorithm is used to precisely detect the HR of rats, mice, humans, frogs and rabbits, and the results show that this method has good performance. In particular, we apply the proposed algorithm to test some ECG signals in humans from the MIT-BIH database. The results show that the accuracy of the proposed algorithm for detection of HR in humans reaches 99.71%.
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Li et al. (2019) studied this question.
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